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I have been reading the 3 parts from "Introduction to RL" section and I have observed in part 3 that the compute_loss function for the Simplest Policy Gradient returns the mean of the product between …
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Remember that one benefit of policy gradient over Q-learning is that it can learn a stochastic policy. and then we don't have to finetune the exploration during training.
note that if we use softma…
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http://arxiv.org/abs/1706.05374
It is equivalent to implement a new Explorer that adds a Gaussian noise whose covariance is ρ_0 exp(cH(s)), where H(s) is a Hessian of Q(s,a) wrt a and ρ_0 and c are…
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From the paper:
in fact the properform for the transition policy gradient arrived at in eqn.10.
manager_loss = -tf.reduce_sum((self.r-cutoff_vf_manager)*dcos) ( from code )
why not implement th…
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DQN was provided as an example. Why was the policy gradient method not provided? Does it work on these problems?
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The current implementation of `policy_gradient_loss` is:
```python
log_pi_a_t = distributions.softmax().logprob(a_t, logits_t)
adv_t = jax.lax.select(use_stop_gradient, jax.lax.stop_gradient(adv_t)…
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When running the code on DMC, because the `actor_grad` is `dynamics`; therefore, `loss_policy` would be `-value_target`. `value_target` is not dependent on the actor's policy distribution, and so, `lo…
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(Updated for clarity)
Apologies if I'm wrong, but it seems to me that there are some mathematical issues in [unit 4 "diving deeper..."](https://github.com/huggingface/deep-rl-class/blob/main/units/en…
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Thank you for your great work!
I refactored the code [repo is here](https://github.com/baichen99/Finite-expression-method/blob/main/train_fex_possion.py), but it seems that the use of policy gradie…
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# Pierre-Luc Bacon
The project description suggests that RLPy is mainly about value function based algorithms. However, I think it'd be nice to add Will Dabney's implementation of some of the popular…